Geographic information systems have traditionally relied on relational database technology. In some applications for geographic information systems, however, information is obtained for geographical features that change as a function of location. For example, with respect to such a feature as road, the number of lanes, the width of the roadbed, the quality of the pavement, the speed limit, the average traffic density are all attributes that change with respect to position on the road. Similarly with respect to other features, such as pipelines, railways, and utility lines, attribute values may vary with respect to position along the feature. Those attributes (such as lane width or pipe diameter) that are subject to change along the length of a feature (such as a road or pipe) are called herein "distributed attributes." To perform "dynamic segmentation" analysis of a linear network involves locating segments (of features) that satisfy user specified search criteria with respect to one or more distributed attributes. The processing of information pertaining to these linear networks to achieve dynamic segmentation poses formidable problems.
Traditional approaches to solving these problems rely on tracking of a relational database in complex ways. For example, one table for a road might include records showing positions on the road as a function of road length. Another table might show different speed limits as a function of each of the positions listed in the first table. Yet another table might contain information about traffic density at each of the positions. Another table might contain information about roadbed width at each position.
It can be seen that such an approach typically requires a first table, identifying positions on the road as a function of length, to have enough entries to permit the recording of meaningful detail of the attribute undergoing the most rapid change as a function of length. As a result, the attribute having the finest granularity typically requires the storage of data pertaining to all other attributes to have the same granularity, regardless of the inherent granularity of those attributes. Such a data structure imposes significant processing demands on a system that is asked, for example, to provide information about any particular attribute, over the length of the feature, since it is likely that the records for the attribute have more detail than necessary, and will not directly show those instances where the attribute changes in value. In addition, queries that involve combinations of attributes are difficult to process.
As an example, consider the following hypothetical database records:
______________________________________ column 1 2 3 4 5 6 ______________________________________ US0123 .vertline. 0.0 .vertline. 9.4 .vertline. 2 .vertline. 12 .vertline. 1954 US0123 .vertline. 9.4 .vertline. 12.4 .vertline. 2 .vertline. 12 .vertline. 1962 US0123 .vertline. 12.4 .vertline. 17.3 .vertline. 2 .vertline. 12 .vertline. 1962 US0123 .vertline. 17.3 .vertline. 21.5 .vertline. 2 .vertline. 12 .vertline. 1962 US0123 .vertline. 21.5 .vertline. 36.4 .vertline. 4 .vertline. 11 .vertline. 1971 ______________________________________
where
column 1=highway name PA1 column 2=begin location for record PA1 column 3=end location for record PA1 column 4=number of lanes PA1 column 5=lane width PA1 column 6=year repaved
Note that although the number of lanes changes only once over the structure of highway covered by those records, additional records are required because the year of repavement, for example, changes more frequently over the length. In this example, a user may wish to inquire of the database as to where the two lane sections of road are along the route. The answer is given in four segments:
______________________________________ from 0.0 to 9.4, from 9.4 to 12.4, from 12.4 to 17.3, and from 17.3 to 21.5. ______________________________________
The computer or user would have to determine that these four segments lie continuously between 0.0 and 21.5. These limitations have traditionally been accepted as inherent in the nature of the data being processed.
Alternative structures have been used for processing geographic information. For example, a single user system is commercially available from Intergraph Corporation of Huntsville, Ala. 35894, under the trademark TIGRIS. TIGRIS is a package of applications, using object-oriented structure, that perform various geographic collection and analysis functions but not dynamic segmentation. Documentation for the TIGRIS system is available from Intergraph Corporation. The TIGRIS system also includes a utility for converting TIGRIS data to and from a relational database format for archival purposes.